Factor analysis

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0.1 Exploratory factor analysis: 7 factors as the number of variables in the study design

0.2 Read in data

all <- read.csv("../02-descriptive_data/merged_filtered_imputedMedian_likertNumber.csv")
rownames(all) <- all$Resp.ID

Seven, is the number of factors that would be present according to the study design. Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.

0.2.1 Likert variables

1 Final Factanal correcting for degree and Context and not for L2

When correcting for context what we are doing is that we are removing the context mean from every context

# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
usable_data <- all[,c(usable_items,"Context","degree")]
usable_data$degree_binary <- ifelse(usable_data$degree %in% c("HUM.SCI","SCI"), "SCI",
                                    ifelse(usable_data$degree %in% "LA","LA","HUM"))
dat_onlyItems <- usable_data[,usable_items]
# get residuals after regressing for context
get_residuals <- function(item,pred1,pred2){
  mod <- lm(item ~ pred1 + pred2)
  return(mod$residuals)
}
applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,
                     pred1=usable_data$Context,pred2=usable_data$degree_binary)

Compare correlation matrix before and after correcting

before <- cor(as.matrix(dat_onlyItems))
after <- cor(applygetRes)
dif <- before - after
hist(dif)

########################
### added
# before <- data.frame(dat_onlyItems)
# after <- data.frame(applygetRes)
# 
# cov <- cor(after,method = "pearson",use="pairwise.complete.obs")
# 
# row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
# row_infos$Variables <- as.character(row_infos$Variables)
# rownames(row_infos) <- rownames(cov)
# row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
# row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
# 
# ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
# ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
# 
# pheatmap(cov, main = "All",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
# ,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
# ### added
# 
# after$Context <- usable_data$Context[match(rownames(after),rownames(usable_data))]
# before$Context <- usable_data$Context[match(rownames(before),rownames(usable_data))]
# 
# ggplot(after,aes(x=fail.ought1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# plot(after$fail.ought1,before$fail.ought1)
# ggplot(before,aes(x=speaking.prof1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# ggplot(before,aes(x=fail.ought1,fill=Context)) + geom_histogram() + facet_wrap(~Context)
# Factanal 
# From a statisticak point of view 
fap <- fa.parallel(applygetRes)

Parallel analysis suggests that the number of factors =  6  and the number of components =  4 

fact <- 6
loading_cutoff <- 0.2
fa_basic <- fa(applygetRes,fact)

Loading required namespace: GPArotation

# analyse residuals vs initial
#fa_basic <- fa(applygetRes, fact,scores="regression")
#fac <- as.matrix(dat_onlyItems) %*% loadings(fa_basic,cutoff = 0.3)
# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic
a2 <- melt(a1,id.vars=c("D"))
a2$inv <- ifelse(a2$value < 0 ,"neg","pos")
a2$value[abs(a2$value) < loading_cutoff] <- 0
a2 <- a2[a2$value!=0,]
a2 <- a2 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a2)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Factors one by one
ggplot(subset(a2,variable %in% "F1"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F1")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F2"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F2") + labs(y="Items")

ggplot(subset(a2,variable %in% "F3"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F3")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F4"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F4")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F5"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F5")+ labs(y="Items")

ggplot(subset(a2,variable %in% "F6"))+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") + ggtitle("F6")+ labs(y="Items")

1.1 Chronbach alpha

f1 <- unique(a2$D[a2$variable %in% "F1"])
f2 <- unique(a2$D[a2$variable %in% "F2"])
f3 <- unique(a2$D[a2$variable %in% "F3"])
f4 <- unique(a2$D[a2$variable %in% "F4"])
f5 <- unique(a2$D[a2$variable %in% "F5"])
f6 <- unique(a2$D[a2$variable %in% "F6"])
psych::alpha(applygetRes[,colnames(applygetRes) %in% f1])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f1])

 

 lower alpha upper     95% confidence boundaries
0.8 0.83 0.86 

 Reliability if an item is dropped:

 Item statistics 

psych::alpha(applygetRes[,colnames(applygetRes) %in% f2])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f2])

 

 lower alpha upper     95% confidence boundaries
0.73 0.77 0.8 

 Reliability if an item is dropped:

 Item statistics 

psych::alpha(applygetRes[,colnames(applygetRes) %in% f3])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f3])

 

 lower alpha upper     95% confidence boundaries
0.67 0.72 0.77 

 Reliability if an item is dropped:

 Item statistics 

psych::alpha(applygetRes[,colnames(applygetRes) %in% f4])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f4])

 

 lower alpha upper     95% confidence boundaries
0.69 0.73 0.78 

 Reliability if an item is dropped:

 Item statistics 

psych::alpha(applygetRes[,colnames(applygetRes) %in% f5])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f5])

 

 lower alpha upper     95% confidence boundaries
0.7 0.74 0.78 

 Reliability if an item is dropped:

 Item statistics 

psych::alpha(applygetRes[,colnames(applygetRes) %in% f6])

Reliability analysis   
Call: psych::alpha(x = applygetRes[, colnames(applygetRes) %in% f6])

 

 lower alpha upper     95% confidence boundaries
0.55 0.62 0.68 

 Reliability if an item is dropped:

 Item statistics 

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
kable(loading_fact_reduced)

	F1	F2	F3	F4	F5	F6
converse.id1		0.44
dream.id1		0.29		0.21	0.21	-0.27
usewell.id1				0.22	0.24
whenever.id1		0.23		0.2	0.34
consider.ought1			0.56
people.ought1			0.53
expect.ought1			0.82
fail.ought1			0.62
enjoy.intr1					0.74
life.intr1					0.55
exciting.intr1		0.21			0.38
challenge.intr1					0.46
job.instru1				0.78
knowledge.instru1					0.22	0.37
career.instru1				0.71
money.instru1				0.53
time.integr1		0.64
becomelike.integr1		0.37				0.27
meeting.integr1		0.56
affinity.integr1		0.68
improve.prof1	0.64
speaking.prof1	0.74
reading.prof1	0.68
written.prof1	0.78
listening.prof1	0.83
citizen.post1						0.56
interact.post1		0.21
overseas.post1	0.29					0.26
globalaccess.post1				0.3		0.33

# predict values per samples from initial likert scale
pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems,applygetRes))
#pred_basic <- as.data.frame(predict(fa_basic,applygetRes))
#https://stackoverflow.com/questions/4145400/how-to-create-factors-from-factanal
#pred_basic <- data.frame(as.matrix(dat_onlyItems) %*% loadings(fa_basic,cutoff=0))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
all_complete_basic <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),])
#match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
#all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_melt <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
ggplot(all_complete_melt) + geom_boxplot(aes(x=Context,y=value,color=Context)) + facet_wrap(~variable) + coord_flip() + guides(color=F)

# error bar 
sum_stat <- all_complete_melt %>% group_by(Context,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Context[!is.na(value)])) %>%
  mutate(stdMean = stdFac/sqrt(nObs),
         CIspread=1.96*stdMean,
         LowerBoundCI = meanFac - 1.96*stdMean,
         UpperBoundCI = meanFac + 1.96*stdMean)
sum_stat$Context <- factor(sum_stat$Context,levels=c("English in Italy", "English in Germany", "Italian in Australia", "German in Australia"))
ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + 
geom_errorbar(aes(ymin=LowerBoundCI, ymax=UpperBoundCI),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI") 

kable(sum_stat)

Context	variable	meanFac	stdFac	nObs	stdMean	CIspread	LowerBoundCI	UpperBoundCI
English in Germany	Factor1	9.180216	1.3385318	70	0.1599852	0.3135709	8.866645	9.493787
English in Germany	Factor2	8.322364	0.8798173	70	0.1051583	0.2061102	8.116254	8.528474
English in Germany	Factor3	1.179170	0.6342022	70	0.0758017	0.1485712	1.030599	1.327741
English in Germany	Factor4	7.508108	0.9099322	70	0.1087577	0.2131651	7.294943	7.721273
English in Germany	Factor5	7.780045	0.8026101	70	0.0959303	0.1880233	7.592022	7.968069
English in Germany	Factor6	3.694089	0.8195410	70	0.0979539	0.1919896	3.502099	3.886079
English in Italy	Factor1	10.123788	0.8109915	91	0.0850150	0.1666294	9.957159	10.290418
English in Italy	Factor2	8.211752	0.8294970	91	0.0869549	0.1704316	8.041320	8.382183
English in Italy	Factor3	1.515703	0.8570981	91	0.0898483	0.1761027	1.339600	1.691805
English in Italy	Factor4	7.762009	0.7910125	91	0.0829206	0.1625245	7.599485	7.924534
English in Italy	Factor5	8.061022	0.8048481	91	0.0843710	0.1653672	7.895655	8.226390
English in Italy	Factor6	4.233386	0.6562653	91	0.0687953	0.1348388	4.098547	4.368225
German in Australia	Factor1	10.151486	0.8241152	88	0.0878510	0.1721879	9.979299	10.323674
German in Australia	Factor2	7.813928	1.0284663	88	0.1096349	0.2148844	7.599044	8.028813
German in Australia	Factor3	1.516524	1.0230719	88	0.1090598	0.2137573	1.302767	1.730281
German in Australia	Factor4	7.170152	1.0085632	88	0.1075132	0.2107259	6.959427	7.380878
German in Australia	Factor5	7.452820	0.9443138	88	0.1006642	0.1973018	7.255519	7.650122
German in Australia	Factor6	3.873541	0.8184000	88	0.0872417	0.1709938	3.702548	4.044535
Italian in Australia	Factor1	10.160109	0.7966791	74	0.0926121	0.1815197	9.978589	10.341629
Italian in Australia	Factor2	8.050933	0.8039070	74	0.0934523	0.1831665	7.867767	8.234099
Italian in Australia	Factor3	1.801576	1.0142178	74	0.1179004	0.2310848	1.570491	2.032661
Italian in Australia	Factor4	6.738630	0.9220862	74	0.1071903	0.2100931	6.528537	6.948723
Italian in Australia	Factor5	7.572769	0.9760002	74	0.1134577	0.2223771	7.350392	7.795146
Italian in Australia	Factor6	3.867960	0.8624561	74	0.1002585	0.1965066	3.671454	4.064467

1.2 Linear models testing the effect of context

pred_basic <- data.frame(pred_basic)
fact_data <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),c("Context","Resp.ID")])
sum(fact_data$Resp.ID != rownames(pred_basic))

[1] 0

summary(lm(Factor1 ~ Context,data=fact_data))

Call:
lm(formula = Factor1 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.3077 -0.5891  0.3183  0.6631  1.7297 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   9.1802     0.1136  80.802  < 2e-16
ContextEnglish in Italy       0.9436     0.1511   6.244 1.36e-09
ContextGerman in Australia    0.9713     0.1522   6.380 6.22e-10
ContextItalian in Australia   0.9799     0.1585   6.183 1.93e-09
                               
(Intercept)                 ***
ContextEnglish in Italy     ***
ContextGerman in Australia  ***
ContextItalian in Australia ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9506 on 319 degrees of freedom
Multiple R-squared:  0.1503,    Adjusted R-squared:  0.1423 
F-statistic: 18.81 on 3 and 319 DF,  p-value: 2.927e-11

summary(lm(Factor2 ~ Context,data=fact_data))

Call:
lm(formula = Factor2 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4043 -0.6275  0.1344  0.6506  1.6875 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   8.3224     0.1068  77.950  < 2e-16
ContextEnglish in Italy      -0.1106     0.1420  -0.779 0.436619
ContextGerman in Australia   -0.5084     0.1431  -3.554 0.000437
ContextItalian in Australia  -0.2714     0.1489  -1.822 0.069319
                               
(Intercept)                 ***
ContextEnglish in Italy        
ContextGerman in Australia  ***
ContextItalian in Australia .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8933 on 319 degrees of freedom
Multiple R-squared:  0.04484,   Adjusted R-squared:  0.03585 
F-statistic: 4.991 on 3 and 319 DF,  p-value: 0.002135

summary(lm(Factor3 ~ Context,data=fact_data))

Call:
lm(formula = Factor3 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9422 -0.6078 -0.1135  0.4796  3.2376 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   1.1792     0.1079  10.927  < 2e-16
ContextEnglish in Italy       0.3365     0.1435   2.345   0.0197
ContextGerman in Australia    0.3374     0.1446   2.333   0.0203
ContextItalian in Australia   0.6224     0.1505   4.135 4.55e-05
                               
(Intercept)                 ***
ContextEnglish in Italy     *  
ContextGerman in Australia  *  
ContextItalian in Australia ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9028 on 319 degrees of freedom
Multiple R-squared:  0.05095,   Adjusted R-squared:  0.04202 
F-statistic: 5.708 on 3 and 319 DF,  p-value: 0.0008119

summary(lm(Factor4 ~ Context,data=fact_data))

Call:
lm(formula = Factor4 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1050 -0.6237  0.0094  0.7025  2.3751 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   7.5081     0.1087  69.050  < 2e-16
ContextEnglish in Italy       0.2539     0.1446   1.756   0.0801
ContextGerman in Australia   -0.3380     0.1457  -2.320   0.0210
ContextItalian in Australia  -0.7695     0.1517  -5.073 6.66e-07
                               
(Intercept)                 ***
ContextEnglish in Italy     .  
ContextGerman in Australia  *  
ContextItalian in Australia ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9097 on 319 degrees of freedom
Multiple R-squared:  0.1517,    Adjusted R-squared:  0.1437 
F-statistic: 19.02 on 3 and 319 DF,  p-value: 2.264e-11

summary(lm(Factor5 ~ Context,data=fact_data))

Call:
lm(formula = Factor5 ~ Context, data = fact_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8463 -0.6276  0.1281  0.7365  1.5365 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                   7.7800     0.1058  73.548   <2e-16
ContextEnglish in Italy       0.2810     0.1407   1.997   0.0467
ContextGerman in Australia   -0.3272     0.1417  -2.309   0.0216
ContextItalian in Australia  -0.2073     0.1476  -1.405   0.1611
                               
(Intercept)                 ***
ContextEnglish in Italy     *  
ContextGerman in Australia  *  
ContextItalian in Australia    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.885 on 319 degrees of freedom
Multiple R-squared:  0.0697,    Adjusted R-squared:  0.06095 
F-statistic: 7.966 on 3 and 319 DF,  p-value: 3.884e-05

summary(lm(Factor6 ~ Context,data=fact_data))

Call:
lm(formula = Factor6 ~ Context, data = fact_data)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.58075 -0.47141  0.05374  0.53462  1.76449 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                  3.69409    0.09409  39.262  < 2e-16
ContextEnglish in Italy      0.53930    0.12515   4.309 2.18e-05
ContextGerman in Australia   0.17945    0.12607   1.423    0.156
ContextItalian in Australia  0.17387    0.13125   1.325    0.186
                               
(Intercept)                 ***
ContextEnglish in Italy     ***
ContextGerman in Australia     
ContextItalian in Australia    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7872 on 319 degrees of freedom
Multiple R-squared:  0.06095,   Adjusted R-squared:  0.05212 
F-statistic: 6.901 on 3 and 319 DF,  p-value: 0.0001625

summary(aov(Factor1 ~ Context,data=fact_data))

             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3   51.0  16.999   18.81 2.93e-11 ***
Residuals   319  288.2   0.904                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(aov(Factor2 ~ Context,data=fact_data))

             Df Sum Sq Mean Sq F value  Pr(>F)   
Context       3  11.95   3.983   4.991 0.00214 **
Residuals   319 254.54   0.798                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(aov(Factor3 ~ Context,data=fact_data))

             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  13.96   4.653   5.708 0.000812 ***
Residuals   319 260.02   0.815                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(aov(Factor4 ~ Context,data=fact_data))

             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  47.22  15.740   19.02 2.26e-11 ***
Residuals   319 264.01   0.828                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(aov(Factor5 ~ Context,data=fact_data))

             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  18.72   6.240   7.966 3.88e-05 ***
Residuals   319 249.87   0.783                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(aov(Factor6 ~ Context,data=fact_data))

             Df Sum Sq Mean Sq F value   Pr(>F)    
Context       3  12.83   4.277   6.901 0.000163 ***
Residuals   319 197.68   0.620                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

1.3 All pairwise comparisons

Independent t-test are performed between every pair of contexts within every factor. The Bonferroni correction is used to adjust the p-values for multiple testing.

pairwise.t.test.with.t.and.df <- function (x, g, p.adjust.method = p.adjust.methods, pool.sd = !paired, 
                                           paired = FALSE, alternative = c("two.sided", "less", "greater"), 
                                           ...) 
{
    if (paired & pool.sd) 
        stop("pooling of SD is incompatible with paired tests")
    DNAME <- paste(deparse(substitute(x)), "and", deparse(substitute(g)))
    g <- factor(g)
    p.adjust.method <- match.arg(p.adjust.method)
    alternative <- match.arg(alternative)
    if (pool.sd) {
        METHOD <- "t tests with pooled SD"
        xbar <- tapply(x, g, mean, na.rm = TRUE)
        s <- tapply(x, g, sd, na.rm = TRUE)
        n <- tapply(!is.na(x), g, sum)
        degf <- n - 1
        total.degf <- sum(degf)
        pooled.sd <- sqrt(sum(s^2 * degf)/total.degf)
        compare.levels <- function(i, j) {
            dif <- xbar[i] - xbar[j]
            se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j])
            t.val <- dif/se.dif
            if (alternative == "two.sided") 
                2 * pt(-abs(t.val), total.degf)
            else pt(t.val, total.degf, lower.tail = (alternative == 
                                                         "less"))
        }
        compare.levels.t <- function(i, j) {
            dif <- xbar[i] - xbar[j]
            se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j])
            t.val = dif/se.dif 
            t.val
        }       
    }
    else {
        METHOD <- if (paired) 
            "paired t tests"
        else "t tests with non-pooled SD"
        compare.levels <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$p.value
        }
        compare.levels.t <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$statistic
        }
        compare.levels.df <- function(i, j) {
            xi <- x[as.integer(g) == i]
            xj <- x[as.integer(g) == j]
            t.test(xi, xj, paired = paired, alternative = alternative, 
                   ...)$parameter
        }
    }
    PVAL <- pairwise.table(compare.levels, levels(g), p.adjust.method)
    TVAL <- pairwise.table.t(compare.levels.t, levels(g), p.adjust.method)
    if (pool.sd) 
        DF <- total.degf
    else
        DF <- pairwise.table.t(compare.levels.df, levels(g), p.adjust.method)           
    ans <- list(method = METHOD, data.name = DNAME, p.value = PVAL, 
                p.adjust.method = p.adjust.method, t.value = TVAL, dfs = DF)
    class(ans) <- "pairwise.htest"
    ans
}
pairwise.table.t <- function (compare.levels.t, level.names, p.adjust.method) 
{
    ix <- setNames(seq_along(level.names), level.names)
    pp <- outer(ix[-1L], ix[-length(ix)], function(ivec, jvec) sapply(seq_along(ivec), 
        function(k) {
            i <- ivec[k]
            j <- jvec[k]
            if (i > j)
                compare.levels.t(i, j)               
            else NA
        }))
    pp[lower.tri(pp, TRUE)] <- pp[lower.tri(pp, TRUE)]
    pp
}

https://stackoverflow.com/questions/27544438/how-to-get-df-and-t-values-from-pairwise-t-test

#f1 <- pairwise.t.test(x = fact_data$Factor1, g = fact_data$Context,p.adjust.method = "none",pool.sd = TRUE)
#kable(f1$p.value,digits = 20)
pair.t.test <- function(x, context,fname = "F1"){
  a <- x[context %in% "English in Germany"]
  b <- x[context %in% "English in Italy"]
  c <- x[context %in% "German in Australia"]
  d <- x[context %in% "Italian in Australia"]
  
  ab <- t.test(a,b,var.equal = TRUE)
  ac <- t.test(a,c,var.equal = TRUE)
  ad <- t.test(a,d,var.equal = TRUE)
  bc <- t.test(b,c,var.equal = TRUE)
  bd <- t.test(b,d,var.equal = TRUE)
  cd <- t.test(c,d,var.equal = TRUE)
  
  test_out <- data.frame(Factor = fname,
                         Context1 = c("English in Germany","English in Germany","English in Germany",
                                      "English in Italy","English in Italy","German in Australia"),
                         Context2 = c("English in Italy","German in Australia","Italian in Australia",
                                      "German in Australia","Italian in Australia","Italian in Australia"),
                         t.value = c(ab$statistic,ac$statistic,ad$statistic,bc$statistic,bd$statistic,cd$statistic),
                         p.value = c(ab$p.value,ac$p.value,ad$p.value,bc$p.value,bd$p.value,cd$p.value),
                         estimate1 = c(ab$estimate[1],ac$estimate[1],ad$estimate[1],bc$estimate[1],bd$estimate[1],cd$estimate[1]),
                         estimate2 = c(ab$estimate[2],ac$estimate[2],ad$estimate[2],bc$estimate[2],bd$estimate[2],cd$estimate[2]),
                         confint1 = c(ab$conf.int[1],ac$conf.int[1],ad$conf.int[1],bc$conf.int[1],bd$conf.int[1],cd$conf.int[1]),
                         confint2 = c(ab$conf.int[2],ac$conf.int[2],ad$conf.int[2],bc$conf.int[2],bd$conf.int[2],cd$conf.int[2]),
                         df = c(ab$parameter,ac$parameter,ad$parameter,bc$parameter,bd$parameter,cd$parameter))
  
  return(test_out)
}
f1 <- pair.t.test(x=fact_data$Factor1,fact_data$Context,fname = "F1")
f2 <- pair.t.test(x=fact_data$Factor2,fact_data$Context,fname = "F2")
f3 <- pair.t.test(x=fact_data$Factor3,fact_data$Context,fname = "F3")
f4 <- pair.t.test(x=fact_data$Factor4,fact_data$Context,fname = "F4")
f5 <- pair.t.test(x=fact_data$Factor5,fact_data$Context,fname = "F5")
f6 <- pair.t.test(x=fact_data$Factor6,fact_data$Context,fname = "F6")
tott <- rbind(f1,f2,f3,f4,f5,f6)
tott$p.adjusted <- p.adjust(tott$p.value,method = "fdr")
kable(tott)

Factor	Context1	Context2	t.value	p.value	estimate1	estimate2	confint1	confint2	df	p.adjusted
F1	English in Germany	English in Italy	-5.5350351	0.0000001	9.180216	10.123788	-1.2802557	-0.6068896	159	0.0000015
F1	English in Germany	German in Australia	-5.6037708	0.0000001	9.180216	10.151486	-1.3136366	-0.6289051	156	0.0000015
F1	English in Germany	Italian in Australia	-5.3719985	0.0000003	9.180216	10.160109	-1.3404789	-0.6193081	142	0.0000028
F1	English in Italy	German in Australia	-0.2266294	0.8209735	10.123788	10.151486	-0.2688904	0.2134940	177	0.8956074
F1	English in Italy	Italian in Australia	-0.2883786	0.7734234	10.123788	10.160109	-0.2850215	0.2123799	163	0.8701013
F1	German in Australia	Italian in Australia	-0.0673497	0.9463874	10.151486	10.160109	-0.2614639	0.2442187	160	0.9940132
F2	English in Germany	English in Italy	0.8169086	0.4152030	8.322364	8.211752	-0.1568090	0.3780334	159	0.4980776
F2	English in Germany	German in Australia	3.2879532	0.0012471	8.322364	7.813928	0.2029853	0.8138862	156	0.0040814
F2	English in Germany	Italian in Australia	1.9342479	0.0550713	8.322364	8.050933	-0.0059728	0.5488347	142	0.0901167
F2	English in Italy	German in Australia	2.8531425	0.0048450	8.211752	7.813928	0.1226576	0.6729895	177	0.0124585
F2	English in Italy	Italian in Australia	1.2557581	0.2110008	8.211752	8.050933	-0.0920617	0.4136991	163	0.2700050
F2	German in Australia	Italian in Australia	-1.6110000	0.1091507	7.813928	8.050933	-0.5275456	0.0535360	160	0.1571770
F3	English in Germany	English in Italy	-2.7550121	0.0065540	1.179170	1.515703	-0.5777844	-0.0952812	159	0.0147465
F3	English in Germany	German in Australia	-2.4136673	0.0169514	1.179170	1.516524	-0.6134368	-0.0612716	156	0.0358971
F3	English in Germany	Italian in Australia	-4.3864589	0.0000223	1.179170	1.801576	-0.9029014	-0.3419114	142	0.0000892
F3	English in Italy	German in Australia	-0.0058302	0.9953548	1.515703	1.516524	-0.2788562	0.2772134	177	0.9953548
F3	English in Italy	Italian in Australia	-1.9621658	0.0514459	1.515703	1.801576	-0.5735623	0.0018152	163	0.0881930
F3	German in Australia	Italian in Australia	-1.7735037	0.0780476	1.516524	1.801576	-0.6024747	0.0323703	160	0.1170714
F4	English in Germany	English in Italy	-1.8907362	0.0604787	7.508108	7.762009	-0.5191181	0.0113150	159	0.0946623
F4	English in Germany	German in Australia	2.1840519	0.0304517	7.508108	7.170152	0.0323038	0.6436071	156	0.0548130
F4	English in Germany	Italian in Australia	5.0372008	0.0000014	7.508108	6.738630	0.4675022	1.0714537	142	0.0000102
F4	English in Italy	German in Australia	4.3766364	0.0000206	7.762009	7.170152	0.3249844	0.8587296	177	0.0000892
F4	English in Italy	Italian in Australia	7.6715730	0.0000000	7.762009	6.738630	0.7599667	1.2867922	163	0.0000000
F4	German in Australia	Italian in Australia	2.8203459	0.0054046	7.170152	6.738630	0.1293558	0.7336891	160	0.0129711
F5	English in Germany	English in Italy	-2.1985578	0.0293541	7.780045	8.061022	-0.5333831	-0.0285713	159	0.0548130
F5	English in Germany	German in Australia	2.3101435	0.0221908	7.780045	7.452820	0.0474314	0.6070185	156	0.0443816
F5	English in Germany	Italian in Australia	1.3875540	0.1674465	7.780045	7.572769	-0.0880246	0.5025768	142	0.2309192
F5	English in Italy	German in Australia	4.6429211	0.0000067	8.061022	7.452820	0.3496880	0.8667163	177	0.0000378
F5	English in Italy	Italian in Australia	3.5221107	0.0005555	8.061022	7.572769	0.2145206	0.7619860	163	0.0019998
F5	German in Australia	Italian in Australia	-0.7930895	0.4289002	7.452820	7.572769	-0.4186380	0.1787402	160	0.4980776
F6	English in Germany	English in Italy	-4.6366600	0.0000073	3.694089	4.233386	-0.7690120	-0.3095822	159	0.0000378
F6	English in Germany	German in Australia	-1.3682880	0.1731894	3.694089	3.873541	-0.4385133	0.0796086	156	0.2309192
F6	English in Germany	Italian in Australia	-1.2386899	0.2175041	3.694089	3.867960	-0.4513498	0.1036077	142	0.2700050
F6	English in Italy	German in Australia	3.2507169	0.0013781	4.233386	3.873541	0.1413889	0.5783006	177	0.0041342
F6	English in Italy	Italian in Australia	3.0896095	0.0023571	4.233386	3.867960	0.1318757	0.5989764	163	0.0065273
F6	German in Australia	Italian in Australia	0.0421876	0.9664018	3.873541	3.867960	-0.2556936	0.2668563	160	0.9940132

fact_data1 <- fact_data[,c("Factor1","Context","Resp.ID")] %>% spread(key = Context, value = Factor1,drop=TRUE)

2 Demographics

Variables have been recoded and we need to do the models.

> demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
> 
> # Combine with demo variables
> pred_basic <- data.frame(pred_basic)
> demo_data <- data.frame(pred_basic,all[match(all$Resp.ID,rownames(pred_basic)),c("Resp.ID",demographics_var)])
> sum(demo_data$Resp.ID != rownames(pred_basic))
> 
> # Gender
> longData <- demo_data %>% gather(key = FactorLabel,value = FactorValue,Factor1:Factor6) %>%
+   group_by(Gender,FactorLabel) %>%
+   summarise(meanDemo = mean(FactorValue),
+             sdDemo =  sd(FactorValue))
> 
> summary(lm(Factor1 ~ prof + Gender + Age + Context,data = demo_data))
> summary(lm(Factor1 ~ Context:prof,data = demo_data))
> summary(lm(Factor2 ~ prof + Context,data = demo_data))
> summary(lm(Factor3 ~ prof + Context,data = demo_data))
> 
> summary(lm(Factor1 ~ origin + Gender + Age + Context,data = demo_data))
> summary(lm(Factor1 ~ Context:prof,data = demo_data))
> summary(lm(Factor2 ~ prof + Context,data = demo_data))
> summary(lm(Factor3 ~ prof + Context,data = demo_data))
> 
> 
> table(dat_fac_demo$L1) # to be changed
> dat_fac_demo$L1_expected <- ifelse(as.character(dat_fac_demo$L1) %in% c("German","Italian","English"),"Yes","No")
> table(dat_fac_demo$L1_expected)
> 
> table(dat_fac_demo$speak.other.L2) # to be changed
> dat_fac_demo$speak.other.L2_binary <- ifelse(!is.na(dat_fac_demo$speak.other.L2) & 
+                                       !(dat_fac_demo$speak.other.L2 %in% c("Yes","No")),"Yes",as.character(all$speak.other.L2))
> table(dat_fac_demo$speak.other.L2_binary)
> 
> #table(dat_fac_demo$study.other.L2) # to be changed
> 
> table(dat_fac_demo$origins)
> 
> table(dat_fac_demo$year.studyL2) # da vedere only for Australian contexts
> 
> table(dat_fac_demo$degree) # to be tried- only interesting for Australia
> # Merge BA in Anglistik  with BA in Nordamerikastudien
> dat_fac_demo$degree1 <- dat_fac_demo$degree
> dat_fac_demo$degree1[dat_fac_demo$degree1 %in% "BA in Nordamerikastudien"] <- "BA in Anglistik"
> 
> table(dat_fac_demo$prof)
> table(dat_fac_demo$L2.VCE)
> 
> #table(dat_fac_demo$other5.other.ways) # to be changed

demo_melt <- melt(all_complete_basic,id.vars = c("Age","Gender","origins","study.year","prof","L2.VCE","Context"),measure.vars = factors)
# age
ageStat <- demo_melt %>% group_by(Context,Age,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Age[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
ageStat$Demo <- "Age"
colnames(ageStat)[2] <- "levels"
ageStat <- data.frame(ageStat)
# Gender
GenderStat <- demo_melt %>% group_by(Context,Gender,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Gender[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
GenderStat$Demo <- "Gender"
colnames(GenderStat)[2] <- "levels"
GenderStat <- data.frame(GenderStat)
# origins
originsStat <- demo_melt %>% group_by(Context,origins,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(origins[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
originsStat$Demo <- "origins"
colnames(originsStat)[2] <- "levels"
originsStat <- data.frame(originsStat)
# study.year
study.yearStat <- demo_melt %>% group_by(Context,study.year,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(study.year[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
study.yearStat$Demo <- "Study Year"
colnames(study.yearStat)[2] <- "levels"
study.yearStat <- data.frame(study.yearStat)
# prof
profStat <- demo_melt %>% group_by(Context,prof,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(prof[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
profStat$Demo <- "Proficiency"
colnames(profStat)[2] <- "levels"
profStat$levels <- as.character(profStat$levels)
profStat <- data.frame(profStat)
# L2.VCE
L2.VCEStat <- demo_melt %>% group_by(Context,L2.VCE,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(L2.VCE[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)

Factor `L2.VCE` contains implicit NA, consider using `forcats::fct_explicit_na`Factor `L2.VCE` contains implicit NA, consider using `forcats::fct_explicit_na`

L2.VCEStat$Demo <- "L2.VCE"
colnames(L2.VCEStat)[2] <- "levels"
L2.VCEStat$levels <- as.character(L2.VCEStat$levels)
L2.VCEStat <- data.frame(L2.VCEStat)
##################
# Combine stats
##################
combine_stat <- rbind(data.frame(L2.VCEStat),data.frame(profStat),study.yearStat,originsStat,ageStat,GenderStat)

2.0.1 Tables

• Age

kable(ageStat)

Context	levels	variable	meanFac	stdFac	nObs	seMean	CI95	Demo
English in Germany	18-25	Factor1	9.180216	1.3385318	70	0.1599852	0.3135709	Age
English in Germany	18-25	Factor2	8.322364	0.8798173	70	0.1051583	0.2061102	Age
English in Germany	18-25	Factor3	1.179170	0.6342022	70	0.0758017	0.1485712	Age
English in Germany	18-25	Factor4	7.508108	0.9099322	70	0.1087577	0.2131651	Age
English in Germany	18-25	Factor5	7.780045	0.8026101	70	0.0959303	0.1880233	Age
English in Germany	18-25	Factor6	3.694089	0.8195410	70	0.0979539	0.1919896	Age
English in Italy	18-25	Factor1	10.141992	0.8087193	88	0.0862098	0.1689712	Age
English in Italy	18-25	Factor2	8.250595	0.8114771	88	0.0865037	0.1695473	Age
English in Italy	18-25	Factor3	1.486580	0.8511478	88	0.0907327	0.1778360	Age
English in Italy	18-25	Factor4	7.766802	0.7884956	88	0.0840539	0.1647457	Age
English in Italy	18-25	Factor5	8.068912	0.8094252	88	0.0862850	0.1691186	Age
English in Italy	18-25	Factor6	4.246956	0.6412113	88	0.0683533	0.1339726	Age
English in Italy	26-30	Factor1	9.589809	0.8392664	3	0.4845507	0.9497193	Age
English in Italy	26-30	Factor2	7.072339	0.5519274	3	0.3186555	0.6245647	Age
English in Italy	26-30	Factor3	2.369975	0.6419463	3	0.3706279	0.7264306	Age
English in Italy	26-30	Factor4	7.621437	1.0396314	3	0.6002315	1.1764537	Age
English in Italy	26-30	Factor5	7.829605	0.7531231	3	0.4348158	0.8522390	Age
English in Italy	26-30	Factor6	3.835341	1.1179977	3	0.6454763	1.2651335	Age
German in Australia	18-25	Factor1	10.146344	0.8317562	86	0.0896906	0.1757935	Age
German in Australia	18-25	Factor2	7.797093	1.0343513	86	0.1115370	0.2186125	Age
German in Australia	18-25	Factor3	1.475515	0.9722664	86	0.1048422	0.2054907	Age
German in Australia	18-25	Factor4	7.172292	1.0040893	86	0.1082737	0.2122165	Age
German in Australia	18-25	Factor5	7.457453	0.9514763	86	0.1026003	0.2010967	Age
German in Australia	18-25	Factor6	3.872224	0.8274412	86	0.0892253	0.1748816	Age
German in Australia	26-30	Factor1	10.674935	NA	1	NA	NA	Age
German in Australia	26-30	Factor2	8.463005	NA	1	NA	NA	Age
German in Australia	26-30	Factor3	1.805787	NA	1	NA	NA	Age
German in Australia	26-30	Factor4	5.898631	NA	1	NA	NA	Age
German in Australia	26-30	Factor5	7.777126	NA	1	NA	NA	Age
German in Australia	26-30	Factor6	4.114784	NA	1	NA	NA	Age
German in Australia	31-35	Factor1	10.070293	NA	1	NA	NA	Age
German in Australia	31-35	Factor2	8.612649	NA	1	NA	NA	Age
German in Australia	31-35	Factor3	4.754093	NA	1	NA	NA	Age
German in Australia	31-35	Factor4	8.257635	NA	1	NA	NA	Age
German in Australia	31-35	Factor5	6.730146	NA	1	NA	NA	Age
German in Australia	31-35	Factor6	3.745585	NA	1	NA	NA	Age
Italian in Australia	18-25	Factor1	10.160109	0.7966791	74	0.0926121	0.1815197	Age
Italian in Australia	18-25	Factor2	8.050933	0.8039070	74	0.0934523	0.1831665	Age
Italian in Australia	18-25	Factor3	1.801576	1.0142178	74	0.1179004	0.2310848	Age
Italian in Australia	18-25	Factor4	6.738630	0.9220862	74	0.1071903	0.2100931	Age
Italian in Australia	18-25	Factor5	7.572769	0.9760002	74	0.1134577	0.2223771	Age
Italian in Australia	18-25	Factor6	3.867960	0.8624561	74	0.1002585	0.1965066	Age

• Gender

kable(GenderStat)

Context	levels	variable	meanFac	stdFac	nObs	seMean	CI95	Demo
English in Germany	Female	Factor1	9.375934	0.9765822	52	0.1354276	0.2654381	Gender
English in Germany	Female	Factor2	8.447988	0.8599845	52	0.1192584	0.2337465	Gender
English in Germany	Female	Factor3	1.206849	0.6015639	52	0.0834219	0.1635069	Gender
English in Germany	Female	Factor4	7.505623	0.9642943	52	0.1337236	0.2620982	Gender
English in Germany	Female	Factor5	7.799138	0.8308182	52	0.1152137	0.2258189	Gender
English in Germany	Female	Factor6	3.692953	0.8296887	52	0.1150571	0.2255120	Gender
English in Germany	Male	Factor1	8.497724	1.9850155	17	0.4814370	0.9436164	Gender
English in Germany	Male	Factor2	7.971708	0.8823214	17	0.2139944	0.4194290	Gender
English in Germany	Male	Factor3	1.088385	0.7544056	17	0.1829702	0.3586217	Gender
English in Germany	Male	Factor4	7.452684	0.7291647	17	0.1768484	0.3466229	Gender
English in Germany	Male	Factor5	7.743595	0.7526882	17	0.1825537	0.3578053	Gender
English in Germany	Male	Factor6	3.725440	0.8289583	17	0.2010519	0.3940618	Gender
English in Germany	Other	Factor1	10.605248	NA	1	NA	NA	Gender
English in Germany	Other	Factor2	7.751078	NA	1	NA	NA	Gender
English in Germany	Other	Factor3	1.283201	NA	1	NA	NA	Gender
English in Germany	Other	Factor4	8.579509	NA	1	NA	NA	Gender
English in Germany	Other	Factor5	7.406884	NA	1	NA	NA	Gender
English in Germany	Other	Factor6	3.220171	NA	1	NA	NA	Gender
English in Italy	Female	Factor1	10.186269	0.8044744	76	0.0922795	0.1808679	Gender
English in Italy	Female	Factor2	8.334574	0.7981082	76	0.0915493	0.1794366	Gender
English in Italy	Female	Factor3	1.468399	0.8543417	76	0.0979997	0.1920794	Gender
English in Italy	Female	Factor4	7.791795	0.7532383	76	0.0864024	0.1693486	Gender
English in Italy	Female	Factor5	8.173769	0.7650786	76	0.0877605	0.1720106	Gender
English in Italy	Female	Factor6	4.285421	0.6212523	76	0.0712625	0.1396745	Gender
English in Italy	Male	Factor1	9.751409	0.7942668	14	0.2122767	0.4160624	Gender
English in Italy	Male	Factor2	7.542614	0.7198600	14	0.1923907	0.3770857	Gender
English in Italy	Male	Factor3	1.796286	0.8766142	14	0.2342850	0.4591986	Gender
English in Italy	Male	Factor4	7.572245	1.0013242	14	0.2676151	0.5245257	Gender
English in Italy	Male	Factor5	7.413260	0.7498194	14	0.2003977	0.3927794	Gender
English in Italy	Male	Factor6	3.963581	0.8113917	14	0.2168535	0.4250329	Gender
English in Italy	Other	Factor1	10.588553	NA	1	NA	NA	Gender
English in Italy	Other	Factor2	8.245202	NA	1	NA	NA	Gender
English in Italy	Other	Factor3	1.182602	NA	1	NA	NA	Gender
English in Italy	Other	Factor4	8.155013	NA	1	NA	NA	Gender
English in Italy	Other	Factor5	8.560954	NA	1	NA	NA	Gender
English in Italy	Other	Factor6	4.056026	NA	1	NA	NA	Gender
German in Australia	Female	Factor1	10.168562	0.8395033	62	0.1066170	0.2089694	Gender
German in Australia	Female	Factor2	7.866677	1.0376375	62	0.1317801	0.2582890	Gender
German in Australia	Female	Factor3	1.608489	1.0137556	62	0.1287471	0.2523443	Gender
German in Australia	Female	Factor4	7.165064	0.9795896	62	0.1244080	0.2438397	Gender
German in Australia	Female	Factor5	7.476764	0.8955129	62	0.1137303	0.2229113	Gender
German in Australia	Female	Factor6	3.894508	0.8770328	62	0.1113833	0.2183112	Gender
German in Australia	Male	Factor1	10.084504	0.8058951	25	0.1611790	0.3159109	Gender
German in Australia	Male	Factor2	7.692401	1.0358125	25	0.2071625	0.4060385	Gender
German in Australia	Male	Factor3	1.286442	1.0512047	25	0.2102409	0.4120723	Gender
German in Australia	Male	Factor4	7.206900	1.1098389	25	0.2219678	0.4350569	Gender
German in Australia	Male	Factor5	7.338295	1.0487657	25	0.2097531	0.4111161	Gender
German in Australia	Male	Factor6	3.809513	0.6809965	25	0.1361993	0.2669506	Gender
German in Australia	Other	Factor1	10.767338	NA	1	NA	NA	Gender
German in Australia	Other	Factor2	7.581668	NA	1	NA	NA	Gender
German in Australia	Other	Factor3	1.566750	NA	1	NA	NA	Gender
German in Australia	Other	Factor4	6.566955	NA	1	NA	NA	Gender
German in Australia	Other	Factor5	8.831433	NA	1	NA	NA	Gender
German in Australia	Other	Factor6	4.174308	NA	1	NA	NA	Gender
Italian in Australia	Female	Factor1	10.297225	0.7153860	58	0.0939348	0.1841122	Gender
Italian in Australia	Female	Factor2	8.100776	0.8162308	58	0.1071764	0.2100657	Gender
Italian in Australia	Female	Factor3	1.744322	1.0319832	58	0.1355060	0.2655918	Gender
Italian in Australia	Female	Factor4	6.760356	0.9430673	58	0.1238308	0.2427084	Gender
Italian in Australia	Female	Factor5	7.589786	0.9483878	58	0.1245294	0.2440777	Gender
Italian in Australia	Female	Factor6	3.912879	0.9089066	58	0.1193453	0.2339168	Gender
Italian in Australia	Male	Factor1	9.593882	0.8851820	15	0.2285530	0.4479639	Gender
Italian in Australia	Male	Factor2	7.839940	0.7706787	15	0.1989884	0.3900172	Gender
Italian in Australia	Male	Factor3	2.051057	0.9667621	15	0.2496169	0.4892491	Gender
Italian in Australia	Male	Factor4	6.598503	0.8595188	15	0.2219268	0.4349765	Gender
Italian in Australia	Male	Factor5	7.432109	1.0920342	15	0.2819620	0.5526455	Gender
Italian in Australia	Male	Factor6	3.694445	0.6888211	15	0.1778528	0.3485916	Gender
Italian in Australia	Other	Factor1	10.700782	NA	1	NA	NA	Gender
Italian in Australia	Other	Factor2	8.324951	NA	1	NA	NA	Gender
Italian in Australia	Other	Factor3	1.380134	NA	1	NA	NA	Gender
Italian in Australia	Other	Factor4	7.580407	NA	1	NA	NA	Gender
Italian in Australia	Other	Factor5	8.695675	NA	1	NA	NA	Gender
Italian in Australia	Other	Factor6	3.865389	NA	1	NA	NA	Gender

• origins

kable(originsStat)

Context	levels	variable	meanFac	stdFac	nObs	seMean	CI95	Demo
English in Germany	No	Factor1	9.142259	1.3714988	65	0.1701135	0.3334224	origins
English in Germany	No	Factor2	8.281116	0.8704514	65	0.1079662	0.2116138	origins
English in Germany	No	Factor3	1.181588	0.6255740	65	0.0775929	0.1520821	origins
English in Germany	No	Factor4	7.494999	0.9291426	65	0.1152460	0.2258821	origins
English in Germany	No	Factor5	7.771619	0.8256632	65	0.1024109	0.2007254	origins
English in Germany	No	Factor6	3.709887	0.8005854	65	0.0993004	0.1946288	origins
English in Germany	Yes	Factor1	9.673656	0.6945173	5	0.3105976	0.6087713	origins
English in Germany	Yes	Factor2	8.858589	0.9180493	5	0.4105641	0.8047057	origins
English in Germany	Yes	Factor3	1.147741	0.8217959	5	0.3675183	0.7203359	origins
English in Germany	Yes	Factor4	7.678528	0.6562043	5	0.2934635	0.5751884	origins
English in Germany	Yes	Factor5	7.889584	0.4341531	5	0.1941592	0.3805519	origins
English in Germany	Yes	Factor6	3.488721	1.1287833	5	0.5048072	0.9894221	origins
English in Italy	No	Factor1	10.114543	0.8106982	90	0.0854551	0.1674920	origins
English in Italy	No	Factor2	8.206219	0.8324542	90	0.0877484	0.1719868	origins
English in Italy	No	Factor3	1.496846	0.8427046	90	0.0888289	0.1741046	origins
English in Italy	No	Factor4	7.758974	0.7949109	90	0.0837910	0.1642303	origins
English in Italy	No	Factor5	8.052053	0.8047709	90	0.0848303	0.1662674	origins
English in Italy	No	Factor6	4.240191	0.6567059	90	0.0692229	0.1356768	origins
English in Italy	Yes	Factor1	10.955859	NA	1	NA	NA	origins
English in Italy	Yes	Factor2	8.709651	NA	1	NA	NA	origins
English in Italy	Yes	Factor3	3.212791	NA	1	NA	NA	origins
English in Italy	Yes	Factor4	8.035203	NA	1	NA	NA	origins
English in Italy	Yes	Factor5	8.868245	NA	1	NA	NA	origins
English in Italy	Yes	Factor6	3.620988	NA	1	NA	NA	origins
German in Australia	No	Factor1	10.231949	0.7717087	63	0.0972262	0.1905633	origins
German in Australia	No	Factor2	7.618197	1.0552275	63	0.1329462	0.2605745	origins
German in Australia	No	Factor3	1.370109	0.9086951	63	0.1144848	0.2243903	origins
German in Australia	No	Factor4	7.053960	1.0218361	63	0.1287392	0.2523289	origins
German in Australia	No	Factor5	7.500587	0.8429098	63	0.1061966	0.2081454	origins
German in Australia	No	Factor6	3.738314	0.7921048	63	0.0997958	0.1955998	origins
German in Australia	Yes	Factor1	9.948720	0.9293495	25	0.1858699	0.3643050	origins
German in Australia	Yes	Factor2	8.307170	0.7770288	25	0.1554058	0.3045953	origins
German in Australia	Yes	Factor3	1.885491	1.2095416	25	0.2419083	0.4741403	origins
German in Australia	Yes	Factor4	7.462958	0.9301706	25	0.1860341	0.3646269	origins
German in Australia	Yes	Factor5	7.332448	1.1730232	25	0.2346046	0.4598251	origins
German in Australia	Yes	Factor6	4.214316	0.7988231	25	0.1597646	0.3131387	origins
Italian in Australia	No	Factor1	10.084062	0.8466602	36	0.1411100	0.2765757	origins
Italian in Australia	No	Factor2	7.914387	0.8738110	36	0.1456352	0.2854449	origins
Italian in Australia	No	Factor3	1.613830	1.0026692	36	0.1671115	0.3275386	origins
Italian in Australia	No	Factor4	6.764422	0.8983703	36	0.1497284	0.2934676	origins
Italian in Australia	No	Factor5	7.650693	0.9790895	36	0.1631816	0.3198359	origins
Italian in Australia	No	Factor6	4.048849	0.8050751	36	0.1341792	0.2629912	origins
Italian in Australia	Yes	Factor1	10.232154	0.7504652	38	0.1217415	0.2386134	origins
Italian in Australia	Yes	Factor2	8.180293	0.7193513	38	0.1166942	0.2287206	origins
Italian in Australia	Yes	Factor3	1.979441	1.0058256	38	0.1631665	0.3198063	origins
Italian in Australia	Yes	Factor4	6.714195	0.9554068	38	0.1549875	0.3037754	origins
Italian in Australia	Yes	Factor5	7.498947	0.9803588	38	0.1590352	0.3117090	origins
Italian in Australia	Yes	Factor6	3.696591	0.8901960	38	0.1444089	0.2830414	origins

• study.year

kable(study.yearStat)

Context	levels	variable	meanFac	stdFac	nObs	seMean	CI95	Demo
English in Germany	1st semester	Factor1	9.180216	1.3385318	70	0.1599852	0.3135709	Study Year
English in Germany	1st semester	Factor2	8.322364	0.8798173	70	0.1051583	0.2061102	Study Year
English in Germany	1st semester	Factor3	1.179170	0.6342022	70	0.0758017	0.1485712	Study Year
English in Germany	1st semester	Factor4	7.508108	0.9099322	70	0.1087577	0.2131651	Study Year
English in Germany	1st semester	Factor5	7.780045	0.8026101	70	0.0959303	0.1880233	Study Year
English in Germany	1st semester	Factor6	3.694089	0.8195410	70	0.0979539	0.1919896	Study Year
English in Italy	1st year	Factor1	10.123788	0.8109915	91	0.0850150	0.1666294	Study Year
English in Italy	1st year	Factor2	8.211752	0.8294970	91	0.0869549	0.1704316	Study Year
English in Italy	1st year	Factor3	1.515703	0.8570981	91	0.0898483	0.1761027	Study Year
English in Italy	1st year	Factor4	7.762009	0.7910125	91	0.0829206	0.1625245	Study Year
English in Italy	1st year	Factor5	8.061022	0.8048481	91	0.0843710	0.1653672	Study Year
English in Italy	1st year	Factor6	4.233386	0.6562653	91	0.0687953	0.1348388	Study Year
German in Australia	1st year	Factor1	10.151486	0.8241152	88	0.0878510	0.1721879	Study Year
German in Australia	1st year	Factor2	7.813928	1.0284663	88	0.1096349	0.2148844	Study Year
German in Australia	1st year	Factor3	1.516524	1.0230719	88	0.1090598	0.2137573	Study Year
German in Australia	1st year	Factor4	7.170152	1.0085632	88	0.1075132	0.2107259	Study Year
German in Australia	1st year	Factor5	7.452820	0.9443138	88	0.1006642	0.1973018	Study Year
German in Australia	1st year	Factor6	3.873541	0.8184000	88	0.0872417	0.1709938	Study Year
Italian in Australia	1st year	Factor1	10.160109	0.7966791	74	0.0926121	0.1815197	Study Year
Italian in Australia	1st year	Factor2	8.050933	0.8039070	74	0.0934523	0.1831665	Study Year
Italian in Australia	1st year	Factor3	1.801576	1.0142178	74	0.1179004	0.2310848	Study Year
Italian in Australia	1st year	Factor4	6.738630	0.9220862	74	0.1071903	0.2100931	Study Year
Italian in Australia	1st year	Factor5	7.572769	0.9760002	74	0.1134577	0.2223771	Study Year
Italian in Australia	1st year	Factor6	3.867960	0.8624561	74	0.1002585	0.1965066	Study Year

• prof

kable(profStat)

Context	levels	variable	meanFac	stdFac	nObs	seMean	CI95	Demo
English in Germany	Advanced	Factor1	8.9718180	1.5713176	38	0.2549014	0.4996067	Proficiency
English in Germany	Advanced	Factor2	8.5188156	0.8260854	38	0.1340088	0.2626572	Proficiency
English in Germany	Advanced	Factor3	1.1248261	0.6062369	38	0.0983446	0.1927554	Proficiency
English in Germany	Advanced	Factor4	7.6658829	0.8812894	38	0.1429640	0.2802095	Proficiency
English in Germany	Advanced	Factor5	7.9375503	0.7265937	38	0.1178691	0.2310234	Proficiency
English in Germany	Advanced	Factor6	3.6131235	0.8709985	38	0.1412946	0.2769375	Proficiency
English in Germany	Intermediate	Factor1	9.4658054	1.2064684	5	0.5395491	1.0575162	Proficiency
English in Germany	Intermediate	Factor2	8.3971644	1.1238905	5	0.5026191	0.9851335	Proficiency
English in Germany	Intermediate	Factor3	1.1462753	0.3615941	5	0.1617098	0.3169512	Proficiency
English in Germany	Intermediate	Factor4	7.5035826	0.5481148	5	0.2451244	0.4804438	Proficiency
English in Germany	Intermediate	Factor5	7.3447820	0.7218051	5	0.3228011	0.6326901	Proficiency
English in Germany	Intermediate	Factor6	4.2208877	0.3896576	5	0.1742602	0.3415500	Proficiency
English in Germany	Upper-intermediate	Factor1	9.4206289	0.9370504	27	0.1803354	0.3534575	Proficiency
English in Germany	Upper-intermediate	Factor2	8.0320244	0.8624412	27	0.1659769	0.3253147	Proficiency
English in Germany	Upper-intermediate	Factor3	1.2617455	0.7160135	27	0.1377969	0.2700818	Proficiency
English in Germany	Upper-intermediate	Factor4	7.2868921	0.9790993	27	0.1884277	0.3693184	Proficiency
English in Germany	Upper-intermediate	Factor5	7.6389760	0.8858635	27	0.1704845	0.3341496	Proficiency
English in Germany	Upper-intermediate	Factor6	3.7104854	0.7850227	27	0.1510777	0.2961123	Proficiency
English in Italy	Advanced	Factor1	10.0450022	0.8021570	23	0.1672613	0.3278322	Proficiency
English in Italy	Advanced	Factor2	8.3272071	0.7027466	23	0.1465328	0.2872043	Proficiency
English in Italy	Advanced	Factor3	1.2983018	0.6492239	23	0.1353725	0.2653302	Proficiency
English in Italy	Advanced	Factor4	7.6913472	0.9783794	23	0.2040062	0.3998522	Proficiency
English in Italy	Advanced	Factor5	8.3835120	0.7459519	23	0.1555417	0.3048618	Proficiency
English in Italy	Advanced	Factor6	4.0067942	0.8215462	23	0.1713042	0.3357563	Proficiency
English in Italy	Elementary	Factor1	10.7316730	0.2869470	2	0.2029022	0.3976882	Proficiency
English in Italy	Elementary	Factor2	6.6947733	0.1118463	2	0.0790873	0.1550110	Proficiency
English in Italy	Elementary	Factor3	0.5452615	0.0207356	2	0.0146623	0.0287381	Proficiency
English in Italy	Elementary	Factor4	6.9603545	0.2167864	2	0.1532911	0.3004506	Proficiency
English in Italy	Elementary	Factor5	7.0648783	0.4524283	2	0.3199151	0.6270336	Proficiency
English in Italy	Elementary	Factor6	3.8789953	0.4947548	2	0.3498445	0.6856951	Proficiency
English in Italy	Intermediate	Factor1	9.8617793	0.9736565	9	0.3245522	0.6361222	Proficiency
English in Italy	Intermediate	Factor2	8.0702928	0.6788276	9	0.2262759	0.4435007	Proficiency
English in Italy	Intermediate	Factor3	1.4754908	1.0336667	9	0.3445556	0.6753289	Proficiency
English in Italy	Intermediate	Factor4	7.5815590	0.7990103	9	0.2663368	0.5220201	Proficiency
English in Italy	Intermediate	Factor5	7.7851747	0.8353883	9	0.2784628	0.5457870	Proficiency
English in Italy	Intermediate	Factor6	4.3672887	0.3214393	9	0.1071464	0.2100070	Proficiency
English in Italy	Upper-intermediate	Factor1	10.1756196	0.7986421	57	0.1057827	0.2073342	Proficiency
English in Italy	Upper-intermediate	Factor2	8.2407273	0.8683437	57	0.1150149	0.2254293	Proficiency
English in Italy	Upper-intermediate	Factor3	1.6438258	0.8901621	57	0.1179049	0.2310935	Proficiency
English in Italy	Upper-intermediate	Factor4	7.8471426	0.7070239	57	0.0936476	0.1835493	Proficiency
English in Italy	Upper-intermediate	Factor5	8.0094025	0.7912184	57	0.1047994	0.2054069	Proficiency
English in Italy	Upper-intermediate	Factor6	4.3161104	0.6102990	57	0.0808361	0.1584387	Proficiency
German in Australia	Advanced	Factor1	10.6305008	0.1903613	4	0.0951807	0.1865541	Proficiency
German in Australia	Advanced	Factor2	8.5881502	0.9889888	4	0.4944944	0.9692090	Proficiency
German in Australia	Advanced	Factor3	2.0015290	1.6060287	4	0.8030144	1.5739081	Proficiency
German in Australia	Advanced	Factor4	7.7057327	1.0522989	4	0.5261494	1.0312529	Proficiency
German in Australia	Advanced	Factor5	7.6357323	1.1159707	4	0.5579853	1.0936513	Proficiency
German in Australia	Advanced	Factor6	3.7133896	1.0305066	4	0.5152533	1.0098965	Proficiency
German in Australia	Elementary	Factor1	10.1168077	0.9064044	32	0.1602312	0.3140531	Proficiency
German in Australia	Elementary	Factor2	7.6501811	1.1416160	32	0.2018111	0.3955498	Proficiency
German in Australia	Elementary	Factor3	1.7631737	1.1534218	32	0.2038981	0.3996402	Proficiency
German in Australia	Elementary	Factor4	7.2927306	1.0145114	32	0.1793420	0.3515103	Proficiency
German in Australia	Elementary	Factor5	7.2999255	1.0797485	32	0.1908744	0.3741138	Proficiency
German in Australia	Elementary	Factor6	4.0011990	0.8029138	32	0.1419364	0.2781954	Proficiency
German in Australia	Intermediate	Factor1	10.2298341	0.7820790	25	0.1564158	0.3065750	Proficiency
German in Australia	Intermediate	Factor2	7.8832030	0.8604999	25	0.1721000	0.3373159	Proficiency
German in Australia	Intermediate	Factor3	1.4811441	0.7509894	25	0.1501979	0.2943879	Proficiency
German in Australia	Intermediate	Factor4	7.2424688	0.9742859	25	0.1948572	0.3819201	Proficiency
German in Australia	Intermediate	Factor5	7.5145537	0.9286726	25	0.1857345	0.3640397	Proficiency
German in Australia	Intermediate	Factor6	3.8848371	0.7601886	25	0.1520377	0.2979939	Proficiency
German in Australia	Upper-intermediate	Factor1	10.0490780	0.8189491	27	0.1576068	0.3089094	Proficiency
German in Australia	Upper-intermediate	Factor2	7.8291557	1.0290861	27	0.1980477	0.3881735	Proficiency
German in Australia	Upper-intermediate	Factor3	1.1851056	0.9335779	27	0.1796672	0.3521476	Proficiency
German in Australia	Upper-intermediate	Factor4	6.8785697	1.0082746	27	0.1940425	0.3803234	Proficiency
German in Australia	Upper-intermediate	Factor5	7.5497705	0.7788880	27	0.1498971	0.2937982	Proficiency
German in Australia	Upper-intermediate	Factor6	3.7355109	0.8790105	27	0.1691657	0.3315647	Proficiency
Italian in Australia	Elementary	Factor1	10.3507740	0.5949701	29	0.1104832	0.2165470	Proficiency
Italian in Australia	Elementary	Factor2	8.0229350	0.8425050	29	0.1564493	0.3066405	Proficiency
Italian in Australia	Elementary	Factor3	1.5731498	1.0289672	29	0.1910744	0.3745059	Proficiency
Italian in Australia	Elementary	Factor4	6.5818719	0.9056561	29	0.1681761	0.3296252	Proficiency
Italian in Australia	Elementary	Factor5	7.5793151	0.8975255	29	0.1666663	0.3266660	Proficiency
Italian in Australia	Elementary	Factor6	3.6640290	0.9666588	29	0.1795040	0.3518279	Proficiency
Italian in Australia	Intermediate	Factor1	10.0855631	0.8814353	29	0.1636784	0.3208097	Proficiency
Italian in Australia	Intermediate	Factor2	8.0082902	0.8190878	29	0.1521008	0.2981176	Proficiency
Italian in Australia	Intermediate	Factor3	1.7371213	1.0600615	29	0.1968485	0.3858230	Proficiency
Italian in Australia	Intermediate	Factor4	6.9176281	0.9645575	29	0.1791138	0.3510631	Proficiency
Italian in Australia	Intermediate	Factor5	7.5780978	1.0924933	29	0.2028709	0.3976270	Proficiency
Italian in Australia	Intermediate	Factor6	4.0383131	0.7613362	29	0.1413766	0.2770981	Proficiency
Italian in Australia	Upper-intermediate	Factor1	9.9496433	0.9217052	16	0.2304263	0.4516356	Proficiency
Italian in Australia	Upper-intermediate	Factor2	8.1789692	0.7384464	16	0.1846116	0.3618387	Proficiency
Italian in Australia	Upper-intermediate	Factor3	2.3324244	0.7228639	16	0.1807160	0.3542033	Proficiency
Italian in Australia	Upper-intermediate	Factor4	6.6983198	0.8711522	16	0.2177881	0.4268646	Proficiency
Italian in Australia	Upper-intermediate	Factor5	7.5512469	0.9505781	16	0.2376445	0.4657833	Proficiency
Italian in Australia	Upper-intermediate	Factor6	3.9288207	0.8082490	16	0.2020622	0.3960420	Proficiency

• L2.VCE

kable(L2.VCEStat)

Context	levels	variable	meanFac	stdFac	nObs	seMean	CI95	Demo
English in Germany	NA	Factor1	9.180216	1.3385318	70	0.1599852	0.3135709	L2.VCE
English in Germany	NA	Factor2	8.322364	0.8798173	70	0.1051583	0.2061102	L2.VCE
English in Germany	NA	Factor3	1.179170	0.6342022	70	0.0758017	0.1485712	L2.VCE
English in Germany	NA	Factor4	7.508108	0.9099322	70	0.1087577	0.2131651	L2.VCE
English in Germany	NA	Factor5	7.780045	0.8026101	70	0.0959303	0.1880233	L2.VCE
English in Germany	NA	Factor6	3.694089	0.8195410	70	0.0979539	0.1919896	L2.VCE
English in Italy	NA	Factor1	10.123788	0.8109915	91	0.0850150	0.1666294	L2.VCE
English in Italy	NA	Factor2	8.211752	0.8294970	91	0.0869549	0.1704316	L2.VCE
English in Italy	NA	Factor3	1.515703	0.8570981	91	0.0898483	0.1761027	L2.VCE
English in Italy	NA	Factor4	7.762009	0.7910125	91	0.0829206	0.1625245	L2.VCE
English in Italy	NA	Factor5	8.061022	0.8048481	91	0.0843710	0.1653672	L2.VCE
English in Italy	NA	Factor6	4.233386	0.6562653	91	0.0687953	0.1348388	L2.VCE
German in Australia	No	Factor1	10.049914	0.9790703	27	0.1884222	0.3693075	L2.VCE
German in Australia	No	Factor2	7.820436	1.2438692	27	0.2393827	0.4691902	L2.VCE
German in Australia	No	Factor3	1.861283	1.2187040	27	0.2345397	0.4596978	L2.VCE
German in Australia	No	Factor4	7.372187	1.1104227	27	0.2137009	0.4188538	L2.VCE
German in Australia	No	Factor5	7.258649	1.1235823	27	0.2162335	0.4238177	L2.VCE
German in Australia	No	Factor6	4.024900	0.9492583	27	0.1826848	0.3580623	L2.VCE
German in Australia	Yes	Factor1	10.213215	0.7616911	48	0.1099406	0.2154837	L2.VCE
German in Australia	Yes	Factor2	7.938875	0.8524114	48	0.1230350	0.2411486	L2.VCE
German in Australia	Yes	Factor3	1.440850	0.9493849	48	0.1370319	0.2685825	L2.VCE
German in Australia	Yes	Factor4	7.113726	1.0136097	48	0.1463020	0.2867518	L2.VCE
German in Australia	Yes	Factor5	7.468157	0.8535962	48	0.1232060	0.2414838	L2.VCE
German in Australia	Yes	Factor6	3.822268	0.7191611	48	0.1038020	0.2034518	L2.VCE
German in Australia	NA	Factor1	10.134523	0.7320653	13	0.2030384	0.3979552	L2.VCE
German in Australia	NA	Factor2	7.339069	1.0787274	13	0.2991852	0.5864029	L2.VCE
German in Australia	NA	Factor3	1.079900	0.5859190	13	0.1625047	0.3185092	L2.VCE
German in Australia	NA	Factor4	6.958886	0.7253853	13	0.2011857	0.3943239	L2.VCE
German in Australia	NA	Factor5	7.799474	0.8124103	13	0.2253221	0.4416312	L2.VCE
German in Australia	NA	Factor6	3.748502	0.8937892	13	0.2478925	0.4858693	L2.VCE
Italian in Australia	No	Factor1	10.284602	0.5720927	20	0.1279238	0.2507307	L2.VCE
Italian in Australia	No	Factor2	7.874895	0.8346340	20	0.1866298	0.3657945	L2.VCE
Italian in Australia	No	Factor3	1.697145	1.1802692	20	0.2639162	0.5172758	L2.VCE
Italian in Australia	No	Factor4	6.743492	0.8269375	20	0.1849089	0.3624214	L2.VCE
Italian in Australia	No	Factor5	7.445237	0.9635308	20	0.2154520	0.4222860	L2.VCE
Italian in Australia	No	Factor6	3.581091	0.9805687	20	0.2192618	0.4297532	L2.VCE
Italian in Australia	Yes	Factor1	10.080101	0.8941083	42	0.1379639	0.2704093	L2.VCE
Italian in Australia	Yes	Factor2	8.072739	0.8012190	42	0.1236308	0.2423163	L2.VCE
Italian in Australia	Yes	Factor3	1.878319	0.9350282	42	0.1442780	0.2827848	L2.VCE
Italian in Australia	Yes	Factor4	6.870583	0.9809297	42	0.1513607	0.2966670	L2.VCE
Italian in Australia	Yes	Factor5	7.585222	1.0525838	42	0.1624172	0.3183377	L2.VCE
Italian in Australia	Yes	Factor6	4.053883	0.7618064	42	0.1175493	0.2303966	L2.VCE
Italian in Australia	NA	Factor1	10.232648	0.7802001	12	0.2252244	0.4414398	L2.VCE
Italian in Australia	NA	Factor2	8.268009	0.7637552	12	0.2204771	0.4321352	L2.VCE
Italian in Australia	NA	Factor3	1.707029	1.0533388	12	0.3040727	0.5959825	L2.VCE
Italian in Australia	NA	Factor4	6.268689	0.7532963	12	0.2174579	0.4262175	L2.VCE
Italian in Australia	NA	Factor5	7.741738	0.7260722	12	0.2095990	0.4108140	L2.VCE
Italian in Australia	NA	Factor6	3.695345	0.8934747	12	0.2579239	0.5055309	L2.VCE

2.0.2 Factor means with Confidence Intervals

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor1")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor1: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor2")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor2: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor3")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor3: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor4")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor4: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor5")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor5: Mean +- 95% CI") + theme_bw()

pos <- position_dodge(width=0.4)
ggplot(subset(combine_stat,variable %in% c("Factor6")),aes(x=levels,y=meanFac,colour=Context,group=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
  geom_point(position=pos) + ggtitle("Factor6: Mean +- 95% CI") + theme_bw()

3 Other tentatives

3.1 FA with 7 factors (as from design)

# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
usable_data <- all[,usable_items]
sum(is.na(usable_data))

[1] 0

# Cronbach's alpha using consistent items across contexts
psych::alpha(usable_data,use="pairwise.complete.obs")

Reliability analysis   
Call: psych::alpha(x = usable_data, use = "pairwise.complete.obs")

 

 lower alpha upper     95% confidence boundaries
0.81 0.84 0.86 

 Reliability if an item is dropped:

 Item statistics 

Non missing response frequency for each item
                      1    2    3    4    5 miss
converse.id1       0.00 0.03 0.10 0.41 0.47    0
dream.id1          0.00 0.00 0.07 0.36 0.56    0
usewell.id1        0.00 0.02 0.11 0.46 0.42    0
whenever.id1       0.00 0.03 0.12 0.37 0.47    0
consider.ought1    0.14 0.40 0.21 0.19 0.06    0
people.ought1      0.09 0.27 0.25 0.28 0.11    0
expect.ought1      0.39 0.44 0.09 0.07 0.01    0
fail.ought1        0.27 0.46 0.16 0.10 0.01    0
enjoy.intr1        0.00 0.01 0.06 0.40 0.54    0
life.intr1         0.02 0.24 0.25 0.36 0.12    0
exciting.intr1     0.00 0.01 0.02 0.37 0.61    0
challenge.intr1    0.00 0.03 0.12 0.48 0.36    0
job.instru1        0.00 0.04 0.32 0.41 0.23    0
knowledge.instru1  0.00 0.01 0.09 0.59 0.32    0
career.instru1     0.00 0.00 0.20 0.41 0.39    0
money.instru1      0.01 0.12 0.55 0.26 0.06    0
time.integr1       0.00 0.01 0.07 0.29 0.63    0
becomelike.integr1 0.03 0.23 0.47 0.18 0.10    0
meeting.integr1    0.00 0.00 0.03 0.37 0.59    0
affinity.integr1   0.01 0.07 0.36 0.39 0.17    0
improve.prof1      0.01 0.02 0.03 0.34 0.59    0
speaking.prof1     0.00 0.01 0.00 0.28 0.71    0
reading.prof1      0.00 0.02 0.02 0.38 0.59    0
written.prof1      0.00 0.01 0.02 0.36 0.62    0
listening.prof1    0.00 0.01 0.04 0.38 0.57    0
citizen.post1      0.01 0.07 0.23 0.46 0.23    0
interact.post1     0.00 0.00 0.06 0.43 0.50    0
overseas.post1     0.00 0.01 0.02 0.34 0.63    0
globalaccess.post1 0.00 0.01 0.06 0.49 0.43    0

fact <- 7
loading_cutoff <- 0.2
fa_basic <- fa(usable_data,fact)
fa_basic

Factor Analysis using method =  minres
Call: fa(r = usable_data, nfactors = fact)
Standardized loadings (pattern matrix) based upon correlation matrix

                       MR2  MR3  MR4  MR7  MR5  MR6  MR1
SS loadings           3.30 1.86 2.00 1.95 1.89 1.79 0.85
Proportion Var        0.11 0.06 0.07 0.07 0.07 0.06 0.03
Cumulative Var        0.11 0.18 0.25 0.31 0.38 0.44 0.47
Proportion Explained  0.24 0.14 0.15 0.14 0.14 0.13 0.06
Cumulative Proportion 0.24 0.38 0.53 0.67 0.81 0.94 1.00

 With factor correlations of 
     MR2   MR3  MR4  MR7   MR5  MR6   MR1
MR2 1.00  0.08 0.10 0.08  0.20 0.24  0.04
MR3 0.08  1.00 0.07 0.08 -0.03 0.02 -0.15
MR4 0.10  0.07 1.00 0.26  0.27 0.42  0.14
MR7 0.08  0.08 0.26 1.00  0.33 0.28  0.12
MR5 0.20 -0.03 0.27 0.33  1.00 0.36  0.20
MR6 0.24  0.02 0.42 0.28  0.36 1.00  0.14
MR1 0.04 -0.15 0.14 0.12  0.20 0.14  1.00

Mean item complexity =  1.8
Test of the hypothesis that 7 factors are sufficient.

The degrees of freedom for the null model are  406  and the objective function was  10.52 with Chi Square of  3278.37
The degrees of freedom for the model are 224  and the objective function was  1.23 

The root mean square of the residuals (RMSR) is  0.03 
The df corrected root mean square of the residuals is  0.04 

The harmonic number of observations is  323 with the empirical chi square  224.23  with prob <  0.48 
The total number of observations was  323  with Likelihood Chi Square =  376.07  with prob <  8.2e-10 

Tucker Lewis Index of factoring reliability =  0.902
RMSEA index =  0.049  and the 90 % confidence intervals are  0.038 0.054
BIC =  -918.12
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy             
                                                   MR2  MR3  MR4
Correlation of (regression) scores with factors   0.95 0.90 0.90
Multiple R square of scores with factors          0.90 0.81 0.82
Minimum correlation of possible factor scores     0.80 0.62 0.63
                                                   MR7  MR5  MR6
Correlation of (regression) scores with factors   0.88 0.89 0.86
Multiple R square of scores with factors          0.77 0.79 0.74
Minimum correlation of possible factor scores     0.53 0.58 0.49
                                                   MR1
Correlation of (regression) scores with factors   0.75
Multiple R square of scores with factors          0.57
Minimum correlation of possible factor scores     0.13

# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
a1$value[abs(a1$value) < loading_cutoff] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced

# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,usable_data))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)

# 7 * 12 rows removed

3.2 Basic factor analysis: 6 factors following the fa.parallel suggestion

Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.

# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
usable_data <- all[,usable_items]
# From a statisticak point of view 
fap <- fa.parallel(usable_data)

Parallel analysis suggests that the number of factors =  6  and the number of components =  4 

fact <- 6
loading_cutoff <- 0.2
fa_basic <- fa(usable_data,fact)
fa_basic

Factor Analysis using method =  minres
Call: fa(r = usable_data, nfactors = fact)
Standardized loadings (pattern matrix) based upon correlation matrix

                       MR2  MR4  MR3  MR5  MR1  MR6
SS loadings           3.35 2.47 1.88 2.11 2.15 1.03
Proportion Var        0.12 0.09 0.06 0.07 0.07 0.04
Cumulative Var        0.12 0.20 0.27 0.34 0.41 0.45
Proportion Explained  0.26 0.19 0.14 0.16 0.17 0.08
Cumulative Proportion 0.26 0.45 0.59 0.76 0.92 1.00

 With factor correlations of 
     MR2  MR4   MR3  MR5   MR1  MR6
MR2 1.00 0.13  0.05 0.11  0.22 0.16
MR4 0.13 1.00  0.00 0.33  0.37 0.24
MR3 0.05 0.00  1.00 0.03 -0.05 0.11
MR5 0.11 0.33  0.03 1.00  0.39 0.16
MR1 0.22 0.37 -0.05 0.39  1.00 0.16
MR6 0.16 0.24  0.11 0.16  0.16 1.00

Mean item complexity =  1.8
Test of the hypothesis that 6 factors are sufficient.

The degrees of freedom for the null model are  406  and the objective function was  10.52 with Chi Square of  3278.37
The degrees of freedom for the model are 247  and the objective function was  1.46 

The root mean square of the residuals (RMSR) is  0.03 
The df corrected root mean square of the residuals is  0.04 

The harmonic number of observations is  323 with the empirical chi square  293.67  with prob <  0.022 
The total number of observations was  323  with Likelihood Chi Square =  448.4  with prob <  7.6e-14 

Tucker Lewis Index of factoring reliability =  0.883
RMSEA index =  0.053  and the 90 % confidence intervals are  0.043 0.058
BIC =  -978.68
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy             
                                                   MR2  MR4  MR3
Correlation of (regression) scores with factors   0.95 0.91 0.90
Multiple R square of scores with factors          0.90 0.82 0.81
Minimum correlation of possible factor scores     0.80 0.65 0.63
                                                   MR5  MR1  MR6
Correlation of (regression) scores with factors   0.88 0.89 0.78
Multiple R square of scores with factors          0.78 0.79 0.61
Minimum correlation of possible factor scores     0.55 0.57 0.22

# plot loadings
loadings_basic <- fa_basic$loadings
class(loadings_basic)<-"matrix"
colnames(loadings_basic)<-paste("F",1:fact,sep="")
loadings_basic<-as.data.frame(loadings_basic)
loadings_basic<-round(loadings_basic,2)
loadings_basic$D <- rownames(loadings_basic)
a1 <- loadings_basic
a1 <- melt(a1,id.vars=c("D"))
a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
a1$value[abs(a1$value) < loading_cutoff] <- 0
a1 <- a1[a1$value!=0,]
a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")

# Table of the factors
loadings_basic$D <- NULL
loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
loadings_basic[loadings_basic=="0"] <- ""
loading_fact_reduced <- loadings_basic
loading_fact_reduced

# predict values per samples
pred_basic <- as.data.frame(predict(fa_basic,usable_data))
names(pred_basic) <- paste("Factor",1:fact,sep = "")
factors <- names(pred_basic)
match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))

# Plot loadings by context
all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
library(ggplot2)
ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)

# 7 * 12 rows removed
# error bar 
sum_stat <- all_complete_basic %>% group_by(Context,variable) %>%
  summarise(meanFac = mean(value,na.rm=TRUE),
            stdFac = sd(value,na.rm=TRUE),
            nObs = length(Context[!is.na(value)])) %>%
  mutate(seMean = stdFac/sqrt(nObs),
         CI95 = 1.96*seMean)
ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI")

ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) + 
geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales="free_y") + 
  geom_point() + ggtitle("Mean +- 95% CI")

kable(sum_stat)

Context	variable	meanFac	stdFac	nObs	seMean	CI95
English in Germany	Factor1	-0.7673653	1.2968739	70	0.1550061	0.3038119
English in Germany	Factor2	0.2464654	0.8811501	70	0.1053176	0.2064224
English in Germany	Factor3	-0.4025772	0.6874385	70	0.0821646	0.1610427
English in Germany	Factor4	0.3070734	0.9509279	70	0.1136576	0.2227689
English in Germany	Factor5	0.0672303	0.8805028	70	0.1052402	0.2062708
English in Germany	Factor6	-0.3916784	1.0244232	70	0.1224420	0.2399863
English in Italy	Factor1	0.1674403	0.7825545	91	0.0820340	0.1607866
English in Italy	Factor2	0.5194919	0.7674261	91	0.0804481	0.1576783
English in Italy	Factor3	0.0054434	0.9238201	91	0.0968427	0.1898116
English in Italy	Factor4	0.0311704	0.9431596	91	0.0988700	0.1937852
English in Italy	Factor5	0.4084333	0.8766512	91	0.0918980	0.1801201
English in Italy	Factor6	0.3768171	0.8018947	91	0.0840614	0.1647604
German in Australia	Factor1	0.2302922	0.7943686	88	0.0846800	0.1659728
German in Australia	Factor2	-0.2004232	1.0176085	88	0.1084774	0.2126158
German in Australia	Factor3	0.0300804	1.1010277	88	0.1173699	0.2300451
German in Australia	Factor4	-0.2897234	1.1273569	88	0.1201766	0.2355462
German in Australia	Factor5	-0.3245639	1.0203688	88	0.1087717	0.2131925
German in Australia	Factor6	-0.0289222	1.0127781	88	0.1079625	0.2116065
Italian in Australia	Factor1	0.2461188	0.7676304	74	0.0892352	0.1749010
Italian in Australia	Factor2	-0.6336365	0.9310097	74	0.1082277	0.2121263
Italian in Australia	Factor3	0.3383511	1.0930484	74	0.1270643	0.2490460
Italian in Australia	Factor4	0.0157300	0.8670613	74	0.1007938	0.1975559
Italian in Australia	Factor5	-0.1798909	1.0572445	74	0.1229022	0.2408883
Italian in Australia	Factor6	-0.0584826	1.0369273	74	0.1205404	0.2362591

3.3 Factor analysis using 6 factors correcting for context and degree (which will become the final)

3.3.1 Check what is the effect of 0 years vs all in year.studyL2

We can see that L2 o vs >1 does not have an effect on the items apart bordeline for dream.

dat <- dat %>%
  dplyr::mutate(item = fct_reorder(item,X4),.desc = FALSE) %>%
  dplyr::rename(Effect = X3,
                low95CI = X1,
                high95CI = X2,
                pval = X4)

Error in length(f) == length(.x) : object 'X4' not found

• Effect and 95%CI for dream

dat[dat$item %in% "dream",]

3.4 Try FA correcting also for L2 (0 vs >0) (on top of Context and degree)

rr rr # items to be used for the FA usable_items <- likert_variables1[!(likert_variables1 %in% c(1,1,.comm1, .comm1, .comm1))]

usable_data <- all[,c(usable_items,,,.studyL2)] usable_data\(degree_binary <- ifelse(usable_data\)degree %in% c(.SCI,), , ifelse(usable_data\(degree %in% \LA\,\LA\,\HUM\)) usable_data\)year.studyL2_binary <- ifelse(usable_data$year.studyL2 == years,0,1) dat_onlyItems <- usable_data[,usable_items]

4 get residuals after regressing for context

get_residuals <- function(item,pred1,pred2,pred3){ mod <- lm(item ~ pred1 + pred2 +pred3) return(mod$residuals) }

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals, pred1=usable_data\(Context,pred2=usable_data\)degree_binary,pred3=usable_data$year.studyL2_binary)

5 Factanal

6 From a statisticak point of view

fap <- fa.parallel(applygetRes) fact <- 6 loading_cutoff <- 0.2 fa_basic <- fa(applygetRes,fact)

fa_basic

7 plot loadings

loadings_basic <- fa_basic\(loadings class(loadings_basic)<-\matrix\ colnames(loadings_basic)<-paste(\F\,1:fact,sep=\\) loadings_basic<-as.data.frame(loadings_basic) loadings_basic<-round(loadings_basic,2) loadings_basic\)D <- rownames(loadings_basic) a1 <- loadings_basic

a2 <- melt(a1,id.vars=c()) a2\(inv <- ifelse(a2\)value < 0 ,,) a2\(value[abs(a2\)value) < loading_cutoff] <- 0 a2 <- a2[a2$value!=0,] a2 <- a2 %>% separate(D,into = c(,),remove=FALSE,sep=[.])

ggplot(a2)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat=)+facet_wrap(~variable,ncol = 2,scales = _y)+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype=,colour=red)

7.1 Factor analysis correcting for context and degree and removing 0 years for year.studyL2

rr rr # items to be used for the FA usable_items <- likert_variables1[!(likert_variables1 %in% c(1,1,.comm1, .comm1, .comm1))]

usable_data <- all[,c(usable_items,,,.studyL2)] dat_onlyItems <- usable_data[,usable_items] dat_onlyItems <- dat_onlyItems[usable_data$year.studyL2 != years,] usable_data <- usable_data[usable_data$year.studyL2 != years,]

8 get residuals after regressing for context

get_residuals <- function(item,pred1,pred2){ mod <- lm(item ~ pred1 + pred2) return(mod$residuals) }

applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals, pred1=usable_data\(Context,pred2=usable_data\)degree)

9 Factanal

10 From a statisticak point of view

fap <- fa.parallel(applygetRes) fact <- 7 loading_cutoff <- 0.2 fa_basic <- fa(applygetRes,fact)

fa_basic

11 plot loadings

loadings_basic <- fa_basic\(loadings class(loadings_basic)<-\matrix\ colnames(loadings_basic)<-paste(\F\,1:fact,sep=\\) loadings_basic<-as.data.frame(loadings_basic) loadings_basic<-round(loadings_basic,2) loadings_basic\)D <- rownames(loadings_basic) a1 <- loadings_basic

a1 <- melt(a1,id.vars=c()) a1\(inv <- ifelse(a1\)value < 0 ,,) a1\(value[abs(a1\)value) < loading_cutoff] <- 0 a1 <- a1[a1$value!=0,] a1 <- a1 %>% separate(D,into = c(,),remove=FALSE,sep=[.])

ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat=)+facet_wrap(~variable,ncol = 2,scales = _y)+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype=,colour=red)

12 Table of the factors

loadings_basic$D <- NULL loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0 for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}

loadings_basic[loadings_basic==] <- \ loading_fact_reduced <- loadings_basic loading_fact_reduced

13 predict values per samples

pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems)) names(pred_basic) <- paste(,1:fact,sep = \)

factors <- names(pred_basic) match_initial_data <- match(all$Resp.ID,rownames(pred_basic)) all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,])) corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = ))

14 Plot loadings by context

all_complete_melt <- melt(all_complete_basic,id.vars = ,measure.vars = factors)

library(ggplot2) ggplot(all_complete_melt)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)

15 error bar

sum_stat <- all_complete_melt %>% group_by(Context,variable) %>% summarise(meanFac = mean(value,na.rm=TRUE), stdFac = sd(value,na.rm=TRUE), nObs = length(Context[!is.na(value)])) %>% mutate(seMean = stdFac/sqrt(nObs), CI95 = 1.96*seMean)

ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) + geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales=_y) + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle(+- 95% CI)

ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) + geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales=_y) + geom_point() + ggtitle(+- 95% CI)

kable(sum_stat)